---
license: apache-2.0
base_model: FireRedTeam/FireRedVAD
tags:
  - voice-activity-detection
  - vad
  - coreml
  - apple
  - ios
  - macos
  - streaming
  - real-time
  - dfsmn
  - firered
pipeline_tag: voice-activity-detection
library_name: coremltools
language:
  - multilingual
---

# FireRedVAD-CoreML

Core ML conversion of [FireRedVAD](https://huggingface.co/FireRedTeam/FireRedVAD) Stream-VAD for real-time voice activity detection on Apple platforms (iOS 16+ / macOS 13+). Converted from the original PyTorch model by [FireRedTeam/FireRedVAD](https://huggingface.co/FireRedTeam/FireRedVAD).

## Model Description

- **Original model:** FireRedVAD by Xiaohongshu (小红书) FireRedTeam
- **Architecture:** DFSMN (Deep Feedforward Sequential Memory Network) — 8 DFSMN blocks + 1 DNN layer
- **Variant:** Stream-VAD (causal, lookahead=0), suitable for real-time streaming
- **Parameters:** ~568K (extremely lightweight)
- **Model size:** 2.2 MB (FP32)
- **Input:** 80-dim log-Mel filterbank features (16kHz, 25ms frame, 10ms shift)
- **Output:** Speech probability [0, 1] per frame
- **Language support:** 100+ languages, 20+ Chinese dialects

## Performance

Results from the FLEURS-VAD-102 benchmark (102 languages, 9,443 audio clips):

| Metric | FireRedVAD | Silero-VAD | TEN-VAD | FunASR-VAD | WebRTC-VAD |
|--------|-----------|-----------|---------|-----------|-----------|
| AUC-ROC | **99.60** | 97.99 | 97.81 | - | - |
| F1 Score | **97.57** | 95.95 | 95.19 | 90.91 | 52.30 |
| False Alarm | **2.69%** | 9.41% | 15.47% | 44.03% | 2.83% |
| Miss Rate | 3.62% | 3.95% | 2.95% | 0.42% | 64.15% |

## Core ML Model Specification

### Inputs

| Name | Shape | Type | Description |
|------|-------|------|-------------|
| `feat` | `[1, 1..512, 80]` | Float32 | Log-Mel filterbank features (dynamic time axis) |
| `cache_0` ~ `cache_7` | `[1, 128, 19]` | Float32 | FSMN lookback cache for each of the 8 layers |

### Outputs

| Name | Type | Description |
|------|------|-------------|
| `probs` | Float32 | Speech probability, shape `[1, T, 1]` |
| `new_cache_0` ~ `new_cache_7` | Float32 | Updated lookback cache |

- **Minimum deployment target:** iOS 16 / macOS 13
- **Compute units:** CPU + Neural Engine

## Conversion

Converted from PyTorch using [coremltools](https://github.com/apple/coremltools) via the export script in [FireRedASR2S](https://github.com/FireRedTeam/FireRedASR2S). The Stream-VAD variant was selected for its causal (no lookahead) property, making it suitable for real-time streaming applications.

## Usage

```swift
import CoreML

// Load model
let model = try FireRedVAD(configuration: .init())

// Initialize caches (8 layers x [1, 128, 19])
var caches = (0..<8).map { _ in
    try! MLMultiArray(shape: [1, 128, 19], dataType: .float32)
}

// Process audio frame by frame
let input = FireRedVADInput(
    feat: fbankFeatures,       // [1, T, 80]
    cache_0: caches[0], cache_1: caches[1],
    cache_2: caches[2], cache_3: caches[3],
    cache_4: caches[4], cache_5: caches[5],
    cache_6: caches[6], cache_7: caches[7]
)
let output = try model.prediction(input: input)
let speechProb = output.probs  // [1, T, 1]

// Update caches for next frame
caches = [
    output.new_cache_0, output.new_cache_1,
    output.new_cache_2, output.new_cache_3,
    output.new_cache_4, output.new_cache_5,
    output.new_cache_6, output.new_cache_7
]
```

For a complete implementation with feature extraction, CMVN normalization, and speech state machine, see [FireRedASRKit](https://github.com/leaker/firered_asr).

## References

- [FireRedVAD (Original Model)](https://huggingface.co/FireRedTeam/FireRedVAD)
- [FireRedASR2S GitHub](https://github.com/FireRedTeam/FireRedASR2S)
- [FireRedASR Paper (arXiv:2501.14350)](https://arxiv.org/abs/2501.14350)
- [DFSMN Paper (arXiv:1803.05030)](https://arxiv.org/abs/1803.05030)

## License

Apache 2.0, following the original [FireRedVAD](https://huggingface.co/FireRedTeam/FireRedVAD) license.